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Recognizing Activities with Multiple Cues

  • Rahul Biswas
  • Sebastian Thrun
  • Kikuo Fujimura
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4814)

Abstract

In this paper, we introduce a first-order probabilistic model that combines multiple cues to classify human activities from video data accurately and robustly. Our system works in a realistic office setting with background clutter, natural illumination, different people, and partial occlusion. The model we present is compact, requires only fifteen sentences of first-order logic grouped as a Dynamic Markov Logic Network (DMLNs) to implement the probabilistic model and leverages existing state-of-the-art work in pose detection and object recognition.

Keywords

Object Recognition Activity Recognition Information Gain Confusion Matrix Markov Random Field 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Rahul Biswas
    • 1
  • Sebastian Thrun
    • 1
  • Kikuo Fujimura
    • 2
  1. 1.Stanford University 
  2. 2.Honda Research Institute 

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